class_docstr: ClipSampler for Ego4d moments. Will return a fixed `window_sec` window
  around the given annotation, shifting where relevant to account for the end
  of the clip/video.
  
  clip_start/clip_end is added to the annotation dict to facilitate future lookups.
class_docstr_tok: ["ClipSampler","for","Ego4d","moments",".","Will","return","a","fixed","`","window_sec","`","window","around","the","given","annotation",",","shifting","where","relevant","to","account","for","the","end","of","the","clip\/video",".","clip_start\/clip_end","is","added","to","the","annotation","dict","to","facilitate","future","lookups","."]
code: def __init__(self, window_sec: float = 0) -> None:
      self.window_sec = window_sec
  
file_path: pytorchvideo/pytorchvideo/data/ego4d/utils.py
imports: import json
  import logging
  from abc import ABC, abstractmethod
  from typing import Any, Dict, Tuple
  from iopath.common.file_io import g_pathmgr
  from pytorchvideo.data.clip_sampling import ClipInfo, ClipSampler
  from pytorchvideo.data.utils import get_logger
label_desc: Use image node_id 1 to create a new MomentsClipSampler object from inherited base classes: ClipSampler with example: obj = MomentsClipSampler(window_sec)